Biology subjects

Frydman, G. H.

Publications and source records attributed to Frydman, G. H..

3 recordsLinked to original sources

bioRxiv2026

Computational modeling of pro-inflammatory cytokine-enhanced blood coagulation

The interplay between inflammation and coagulation is a central driver of thrombotic risk across various diseases. While mathematical models of blood coagulation are well established, there remains a critical gap in quantitative frameworks that capture inflammation-induced hypercoagulability. In this study, we develop a mathematical model that explicitly simulates the interaction between pro-inflammatory cytokines and the coagulation cascade. The model incorporates key mechanisms, including: (i) upregulation of tissue factor (TF) by IL-1{beta}, IL-6, and TNF-; (ii) suppression of natural anticoagulants, namely antithrombin III (ATIII) and tissue factor pathway inhibitor (TFPI), by IL-6 and TNF-; and (iii) feedback amplification of proinflammatory cytokines by thrombin. By encoding the bidirectional feedback between inflammatory and coagulation pathways, the model captures essential features of inflammation-driven hypercoagulability and enables systematic quantification of how variability in inflammatory extent and duration results in heterogeneous thrombin generation (TG) dynamics. To evaluate its effectiveness, we integrate the model with TG assays and apply it to virtual patient cohorts representing four clinically distinct conditions: COVID-19, sickle cell disease (SCD), type 2 diabetes mellitus (T2DM) and Hemophilia A. Model simulations predict that disease-specific inflammatory environments induce distinct shifts in TG dynamics. In COVID-19 and T2DM, elevated cytokine levels lead to shortened lag times and increased thrombin peak, whereas in SCD, shortened lag times are accompanied by a reduced thrombin peak. These effects are strongly modulated by both cytokine concentration and duration of exposure. These results demonstrate that the proposed computational model augments conventional TG assays by mechanistically linking inflammatory signaling to disease-specific coagulation responses. Collectively, the proposed computational framework extends conventional TG assays by considering the interplay between inflammation and coagulation, thereby providing a potential tool for predicting disease progression and identifying disease-specific therapeutic targets to advance personalized management strategies in thrombo-inflammatory disorders.

biochemistry↗

bioRxiv2025

Automatic Classification of Circulating Blood Cell Clusters based on Multi-channel Flow Cytometry Imaging

Circulating blood cell clusters (CCCs) containing red blood cells (RBCs), white blood cells (WBCs), and platelets are significant biomarkers linked to conditions like thrombosis, infection, and inflammation. Flow cytometry, paired with fluorescence staining, is commonly used to analyze these cell clusters, revealing cell morphology and protein profiles. While computational approaches based on machine learning have advanced the automatic analysis of single-cell flow cytometry images, there is a lack of effort to build tools to automatically analyze images containing CCCs. Unlike single cells, cell clusters often exhibit irregular shapes and sizes. In addition, these cell clusters often consist of heterogeneous cell types, which require multi-channel staining to identify the specific cell types within the clusters. This study introduces a new computational framework for analyzing CCC images and identifying cell types within clusters. Our framework uses a two-step analysis strategy. First, it categorizes images into cell cluster and non-cluster groups by fine-tuning the You Only Look Once(YOLOv11) model, which outperforms traditional convolutional neural networks (CNNs), Vision Transformers (ViT). Then, it identifies cell types by overlaying cluster contours with regions from multi-channel fluorescence stains, enhancing accuracy despite cell debris and staining artifacts. This approach achieved over 95% accuracy in both cluster classification and phenotype identification. In summary, our automated framework effectively analyzes CCC images from flow cytometry, leveraging both bright-field and fluorescence data. Initially tested on blood cells, it holds potential for broader applications, such as analyzing immune and tumor cell clusters, supporting cellular research across various diseases.

bioinformatics↗

bioRxiv2019

Megakaryocytes Display Innate Immune Cell Functions and Respond during Sepsis

Megakaryocytes (MKs) are precursors to platelets, the second most abundant cells in the peripheral circulation. However, while platelets are known participate in immune responses and play significant roles during infections, the role of MKs within the immune system has not been explored. Here we utilize in vitro techniques to show that both cord blood-derived MKs (CB MKs) and MKs from a human megakaryoblastic leukemia cell line (Meg-01) chemotax towards pathogenic stimuli, phagocytose bacteria, and release chromatin webs in response to bacteria. Moreover, in patients with sepsis, we found that MK counts were significantly higher in the peripheral blood, and CD61+ staining was increased in the kidneys and lungs, correlated with the development of organ dysfunction. Overall, our study suggests that MK cells display basic innate immune cell functions and respond during infections and sepsis.

immunology↗